Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
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Results of gridded ecosystem simulations of bioenergy crops are used for estimating economic viability, environmental impacts, and potential land use change. Gridded model uncertainty propagates through these uses, thus we propose a simple method for estimating regional, spatial model error from sparse field data. We apply this method to the Agricultural-BioGeochemical Cycles (Agro-BGC) model to examine and reduce the model uncertainty associated with grid scale for simulated switchgrass yields in a 6^o latitude x 5^o longitude (~300,000 km^2) region covering Illinois, United States of America. Based on three evaluation sites, changes in yield with scale result from complex intra-model interactions driven by a combination of meteorological rather than soil or terrain variables. Spatial bias of the regional mean significantly increases with increasing cell size for 11 of 15 measurement dates. This bias is primarily due to grid scale, thus bias correction of output yield reduces the model uncertainty associated with grid scale. The corresponding Root Mean Squared Error and Bias-Corrected RMSE (RMSE"B"C) have effectively negligible trends with inconsistent signs. The range of RMSE"B"C for 2-year Average Mature August Yield (AMAY) is 267-285 g C m^-^2 across 3- to 3600-arcsec resolution (~90 m-~100 km) with biases from 9 to 61 g C m^-^2. AMAY bias significantly increases with increasing cell size. Spatial bias of the regional mean is relatively consistent for resolutions @?1200 arcsec (~33 km) (AMAY bias